Industrial Applications of Causal AI

Overview

Discover how Causal AI is transforming industries through practical applications, methodologies, and real-world case studies presented by leading researcher Utkarshani Jaimini.

Syllabus

• Introduction to Causal AI
Definition and Overview of Causal AI
Key Differences Between Causal AI and Traditional AI
Importance and Applications in Various Industries
• Core Concepts of Causality
Causal Inference Basics
Causal Graphs and Directed Acyclic Graphs (DAGs)
Counterfactual Reasoning
• Methodologies for Causal AI
Data Collection and Preprocessing for Causal Analysis
Causal Discovery Techniques
Tools and Frameworks for Causal AI
• Real-world Applications in Industries
Healthcare: Drug Discovery and Patient Treatment Optimization
Finance: Fraud Detection and Risk Management
Marketing: Customer Behavior Analysis and Targeted Advertising
Supply Chain: Demand Forecasting and Inventory Optimization
• Case Studies by Utkarshani Jaimini
Successful Implementation of Causal AI in Industry
Lessons Learned and Challenges Encountered
Best Practices for Applying Causal AI
• Ethical Considerations and Challenges in Causal AI
Bias and Fairness in Causal Models
Transparency and Interpretability
Data Privacy and Security Concerns
• Hands-on Lab Sessions
Setting Up Causal Inference Experiments
Evaluating Causal Models with Real-world Data
Utilizing Popular Causal AI Tools and Libraries
• Future Trends and Innovations in Causal AI
Emerging Research Areas
Potential Industry Disruptions
Integration with Other AI Technologies
• Summary and Review
Recap of Key Concepts and Applications
Open Discussion and Q&A with Utkarshani Jaimini

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Subjects

Data Science